GAN-Augmented Radiomics and Machine Learning for Post-Therapy GBM Progression Assessment

Information from the abstract

Objective: Differentiating true progression (TP) from pseudoprogression (PsP) in post-therapy of glioblastoma multiforme (GBM) remains a critical challenge in neuro-oncology, due to their overlapping radiological characteristics. Consequently, an accurate, non-invasive differentiation between PsP and TP is vital for informing treatment decisions and improving patient outcomes. Therefore, we aimed to present a comprehensive AI-driven framework integrating handcrafted radiomics, deep learning features, and generative modelling to enhance diagnostic precision. Methods: Multiparametric MRI data (T1, T2, FLAIR, T1GD) from 58 GBM patients (41 TP, 17 PsP) were analysed using three feature extraction strategies: handcrafted radiomics, Vision Transformer (ViT)-based deep features, and a hybrid combination of both. To address class imbalance, synthetic samples were generated using a Conditional Tabular Generative Adversarial Network (CT-GAN). Feature dimensionality was reduced through a two-stage pipeline comprising a Variational Autoencoder (VAE) followed by Principal Component Analysis (PCA), preserving 95% variance. Classifiers, including Support Vector Machine (SVM), Random Forest, XGBoost, and Multi-Layer Perceptron (MLP), were trained and validated via stratified 5-fold cross-validation. Results: The radiomics-based SVM model yielded the best performance with an accuracy of 91.84% ± 3.59 (95% CI - 0.841 to 0.989) and an AUC of 0.9667 ± 0.0378 (95% CI - 0.920 to 0.998), demonstrating promising performance within the studied dataset and highlighting potential for clinical application, subject to further external validation. Conclusion: Our demonstrated CT-GAN-augmented hybrid radiomics framework offers a robust, non-invasive, and promising solution for reliably distinguishing PsP from TP. It shows promising performance within the studied dataset, with potential for future clinical translation subject to external validation.

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Related topics: Glioma Diagnosis and Treatment · Radiomics and Machine Learning in Medical Imaging · Brain Metastases and Treatment